With the development of remote sensing and geostatistical technology, complex environmental variables are increasingly easily quantified and applied in modelling soil organic carbon (SOC). However, this emphasizes data redundancy and multicollinearity problems adding to the difficulty in selecting dominant influential auxiliary variables and uncertainty in estimating SOC stocks. The current paper considers the spatial characteristics of SOC density (SOCD) to construct prediction models of SOCD on the basis of reducing the data dimensionality and complexity using the principal component analysis (PCA) method. A total of 260 topsoil samples were collected from Chahe town, China. Eight environmental variables (elevation, aspect, slope, normalized difference vegetation index, normalized difference moisture index, nearest distance to construction area and road, and land use degree comprehensive index) were pre-analysed by PCA and then extracted as the main principal component variables to construct prediction models. Two geostatistical approaches (ordinary kriging and ordinary co-kriging) and two regression approaches (ordinary least squares and geographically weighted regression (GWR)) were used to estimate SOCD. Results showed that PCA played an important role in reducing the redundancy and multicollinearity of the auxiliary variables and GWR achieved the highest prediction accuracy in these four models. GWR considered not only the spatial characteristics of SOCD but also the related valuable information of the auxiliary attributes. In summary, PCA-GWR is a promising spatial method used here to predict SOC stocks.

In contrasting landscapes, the ignorance of diverse relationships between environmental covariates and soil organic matter (SOM) will likely lead to a great deal of prediction uncertainty. To deal with this issue, this study aimed to develop a method to predict SOM in a plain-to-hill transition belt through defining a landform-based stratified model (i.e., separate estimation for different landforms). Initially, the area was split into two strata based on landform types (low-relief areas and hill areas). And then in each stratum the dominant environmental variables were determined. Finally, geographically weighted regression (GWR) was applied to explore the relationships between SOM and environmental variables for the whole study area as well as for each stratum. The results showed that the dominant variables for each stratum were different. The model with stratification outperformed the model without stratification with regards to mean error (0.1 vs. 1.0, respectively), mean absolute error (3.1 vs. 3.8, respectively) and root mean square error (4.1 vs. 5.4, respectively). We conclude that the developed strategy that based on landscape stratification and GWR will be useful for predicting SOM in areas with high variation in topography.

Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0–30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350–2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran’s I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS.